1. A Social-Network-Aided Efficient Peer-to-Peer Live Streaming System IEEE/ACM TRANSACTIONS ON NETWORKING, JUNE 2015 Haiying Shen, Yuhua Lin Dept. of Electrical and Computer Engineering, Clemson University, USA. Jin Li Principal Researcher managing the Multimedia Communication and Storage Team with Microsoft Research, Redmond, WA, USA. Speaker: Yi-Ting Chen

2. Outline Introduction –Social-network-Aided efficient liVe strEaming system (SAVE) Design of the SAVE System Performance Evaluation Conclusions 2

3. Introduction In current P2P live streaming systems, to watch a new channel, a node needs to contact the centralized server in order to join in the channel's overlay. Incurs a large amount of communication overhead on the server. 3

4. Introduction The current wide coverage of broadband Internet enables users to enjoy live streaming programs smoothly. The increase of channels triggers users' desire of watching multiple channels successively or simultaneously. A typical multichannel interface contains one main view and one or more secondary views. 4

5. Introduction Most current P2P live streaming systems only allow users to share the stream in one channel. As a node opens more channels, its maintenance cost for overlay connections increases dramatically. The server receives more requests from nodes to join in new channels. Delayed response leads to inefficiency in P2P live streaming systems 5

6. Related Works 6 P2P live streaming protocols fall into four categories: –Tree-based [21]–[24], –Mesh-based [31]–[32], –Hybrid structure combining both mesh and tree structures –Distributed hash table (DHT)- based structure [20].

7. Tree-based methods Delivering video content via push mechanism, in which parent nodes forward received chunks to their children. 7 [21] Y. Chu, A. Ganjam, T. Ng, S. Rao, K. Sripanidkulchai, J. Zhang, and H. Zhang, “Early experience with an internet broadcast system based on overlay multicast,” in Proc. USENIX, 2004, p. 12. [22] S. Banerjee, B. Bhattacharjee, and C. Kommareddy, “Scalable application layer multicast,” in Proc. SIGCOMM, 2002, pp. 205–217. [23] Y. Chu, S. Rao, and H. Zhang, “A case for end system multicast,” in Proc. ACM SIGMETRICS, 2000, pp. 1–12. [24] R. Tian, Q. Zhang, Z. Xiang, Y. Xiong, X. Li, and W. Zhu, “Robust and efficient path diversity in application-layer multicast for video streaming,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 8, pp. 961–972, Aug. 2005.

8. Mesh-based methods Connecting nodes in a random manner to form a mesh structure. Each node usually serves a number of nodes while also receiving chunks from other nodes. Mesh-based methods are resilient to churn, but generate high overhead by frequent content publishing. 8 [31] L. Massoulie, A. Twig, C. Gkantsidis, and P. Rodriguez, “Randomized decentralized broadcasting algorithms,” in Proc. IEEE INFOCOM, 2007, pp. 1073–1081. [32] F. Picconi and L. Massoulie, “Is there a future for mesh-based live video streaming?,” in Proc. P2P, 2008, pp. 289–298

9. Distributed hash table (DHT)- based structure 9 [20] H. Shen, Z. Li, and J. Li, “A DHT-aided chunk-driven overlay for scalable and efficient peer-to-peer live streaming,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 11, pp. 2125– 2137, Nov. 2012.

10. Multichannel P2P Live Streaming Techniques Chen et al. [19] proposed OAZE. Each peer maintains connections to other physically close peers in a certain number of channels, which its associated user is likely to watch. When a node wants to switch to a channel, it tries to find neighbors connecting to the target channel for the switch. 10 [19] Y. Chen, E. Merrer, Z. Li, Y. Liu, and G. Simon, “OAZE: A networkfriendly distributed zapping system for peer-to-peer IPTV,” Comput. Netw., vol. 56, no. 1, pp. 365–377, 2012.

11. Main Contributions In this paper, we aim to improve the efficiency and scalability of P2P live streaming systems with many users engaging in many successive-channel/ multichannel watching by releasing the load on the centralized server. 11

12. Social-network-Aided efficient liVe strEaming system (SAVE) The key of its design is the utilization of social network concepts. Two main schemes: –Channel Clustering Scheme –Friendlist Scheme 12

13. Outline Introduction –Social-network-Aided efficient liVe strEaming system (SAVE) Design of the SAVE System Performance Evaluation Conclusions 13

14. Overview of the SAVE structure 14

15. Calculates the channel closeness 15 t(x)t(y) t(x)t(y) Three factors: –1) The age (i.e., freshness) –2) The time period that the node stays in both channels –3) If both the channels are in the node's interested channel list.

16. Calculates the channel closeness 16 t(x)t(y) t(x)t(y)

17. Calculates the channel closeness 17

18. Calculates the channel closeness 18

19. Channel Clustering Scheme 19 Aim: To generate clusters –The number of intracluster interactions is maximized –The number of intercluster interactions is minimized. 1) Centralized Channel Clustering 2) Decentralized Channel Clustering

20. Centralized Channel Clustering 20

21. Decentralized Channel Clustering Aim: Generating and maintaining a stable state for the created clusters –Small sum of intercluster closeness. –Relatively large sum of intracluster channel closeness. 21

22. Decentralized Channel Clustering 22

23. Decentralized Channel Clustering 23

24. Decentralized Channel Clustering 24

25. Friendlist Construction 25 SAVE requests users to fill their interest tags manually when they initially join in the system and to periodically update their tags.

26. Similarity 26 is the similarity between their channel lists. is the similarity of their active vectors

27. Efficient Multichannel Video Streaming 27

28. Channel-Closeness-Based Chunk-Pushing 28 Xu et al. [50] showed that when the cache used for a channel reaches 660 kB, the cache hit rate nearly reaches 100%. 660/k-chunk cache can be used for a channel not being watched. k is determined so that the time for viewing chunks can cover the time for finding chunk providers to which to connect. [50] K. Xu, M. Zhang, J. Liu, Z. Qin, and M. Ye, “Proxy caching for peerto- peer live streaming,” Comput. Netw., vol. 54, no. 7, pp. 1229–1241, 2010.

29. Performance Evaluation Used the event-driven simulator PeerSim [17] The P2P live streaming system consists –10000 nodes –100 channels –default video bit rate: 600 kb/s 29 [17] “The PeerSim simulator,” 2013 [Online]. Available: http://peersim.sf.net

30. Performance Evaluation Also built SAVE prototypes on the PlanetLab [18] real-world testbed. –300 online nodes –30 channels Each test lasts for 24 h. 30 [18] “PlanetLab,” [Online]. Available: http://www.planet-lab.org/

31. Switch Delay and Server Load 31 On PeerSim

32. Switch Delay and Server Load 32 on the PlanetLab

33. Effectiveness of the Social Network in SAVE 33 On PeerSim

34. Effectiveness of the Social Network in Save 34 on the PlanetLab

35. Impact of friendlist on PeerSim 35

36. Impact of friendlist on PlanetLab 36

37. Overhead vs. node churn rate 37

38. Channel-Closeness-Based Chunk Pushing 38

39. Channel-Closeness-Based Chunk Pushing 39

40. Conclusions In this paper, we propose SAVE, a social-network- aided efficient P2P live streaming system. SAVE supports successive and multiple-channel viewing with low switch delay and low server overhead by enhancing the operations of joining and switching channels. 40

41. Thanks for your listening! 41